Typically video signal processing circuitry is designed by assuming a desired transfer function and then determining the circuit elements necessary to realize the function. For example, in designing a low pass filter certain "necessary" parameters are assumed such as bandwidth, phase linearity ripple, etc. Then with the aid of design tools the filter is synthesized and finally tested to determine whether it provides the assumed response characteristics. The problem with this approach is that the preliminary assumptions may not comport with the desired response.
A better approach is to assemble circuitry capable of a variety of responses, and then train it to produce desired output signals for a representative set of input signals. The training set must truly represent the conditions found in actual operation, and the system should be sufficiently flexible to generalize to input signals not present during training. Once the training has been accomplished, the circuit parameters may be utilized to fabricate circuitry to emulate the "trained" system.
A class of circuitry termed "neural networks" is capable of being trained to produce desired output responses to sets of input signals. A generalized description of such networks may be found in "Neural Nets for Adaptive Filtering and Adaptive Pattern Recognition" by Bernard Widrow et al., Computer, March 1988, pp. 25-39, and "An Introduction to Computing with Neural Nets" by Richard P. Lippmann, IEEE ASSP Magazine, April 1987, pp. 4-22, which are incorporated herein by reference.
Neural nets are conducive to recognizing patterns or particular signal signatures and have typically been utilized in pattern recognition application. In general neural nets have not been employed as linear signal processing elements, since the component parts (perceptrons) are inherently non-linear.
The present inventors have found that neural nets may be advantageously applied in processing of video signals, in both linear and non-linear signal paths.